MétaCan
Menu
Back to cohort
Record W3203694478 · doi:10.4324/9780203403631-15

ʻIn England we did nursingʼ: Caribbean and British nurses in Great Britain and Canada, 1950–70

2004· book-chapter· en· W3203694478 on OpenAlexaboutno aff
Margaret Shkimba, Karen Flynn

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceImmigrationEconomic shortageFellAttritionWork (physics)Nursing shortageWorld War IIEmigrationPolitical scienceCompetition (biology)Economic growthNursingBusinessMedicineNurse educationGeographyEconomics

Abstract

fetched live from OpenAlex

In the aftermath of the Second World War, the supply of trained nurses available for work fell far short of the demand for their services. This shortage can be attributed to various influences; however, an increased demand in industrializing countries for institutional nurses, high occupational attrition and competition for women labourers from other, more attractive, occupations were prominent causal factors. Owing to the nursing shortage, efforts to induce young women into the profession were pursued through many channels and in many countries. This proved beneficial for thousands of young women by offering to them the opportunity to enter into a career that provided them with a skilled education, certain employment and the chance to travel to countries around the world. Both Canada and the UK experienced chronic shortages in their nursing workforce and for both countries part of the solution appeared in the form of immigrant nursing labour; nurses went in sizeable numbers from the Caribbean to the UK, and from the Caribbean and the UK to Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.327
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicGlobal Health Workforce IssuesFrench-language works237,207